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Elias Bareinboim is an Adjunct Assistant Professor in the Department of Computer Science at Purdue University. His research centers on causal counterfactual inference and its applications in data-driven fields such as medicine, economics, and cognitive science. He has broad interests that encompass artificial intelligence, machine learning, statistics, robotics, and the philosophy of science. His doctoral thesis proposed a general solution to the 'data fusion' problem, which provides practical methods for combining datasets generated under varied experimental conditions. Barenboim's work has been recognized through several awards including AI's 10 to Watch by IEEE, the Dan David Prize Scholarship, the Yahoo! Key Scientific Challenges Award, and the 2014 AAAI Outstanding Paper Award. His published works include notable papers on causal inference and selection bias, presented at prestigious conferences and journals. He has been part of the Purdue faculty since Fall 2015, contributing to the academic community with both teaching and impactful research in computational methodologies across various disciplines.
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